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Tural Sadigov
Statistician, Mathematician and aspiring Data Scientist
Professional Summary
Bio
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- Visiting Assistant Professor of Mathematics and Statistics with Ph.D. in Applied Mathematics and 5+ years experience in teaching data-related courses such as various levels of Statistics, Machine Learning, Probability and Time Series Analysis, and mentoring undergraduate Machine Learning projects.
Current positions
Visiting Assistant Professor of Mathematics and Statistics
Hamilton College
Clinton, NY
present - 2020
- Teaching data science courses including Statistical Modeling and Its Applications and Statistical Methods in Machine Learning. Also supervising students’ machine learning projects.
- Also teaching other mathematics courses such as all levels of calculus
Coursera Instructor
Coursera
Online
present - 2017
- Co-teaching Practical Time Series Analysis with William Thistleton
Previous positions
Lecturer of Statistics
Hamilton College
Clinton, NY
2020 - 2019
- Teaching Statistical Analysis of Data
Lecture of Applied Mathematics
SUNY Polytechnic Institue
Utica, NY
2020 - 2015
- Teaching data science, statistics and applied mathematics courses
- Applied Probability, Regression, Time Series Analysis, Linear Algebra, Calculus, Differential Equations
- Both undergraduate and graduate level courses
Mathematics Service Coordinator
SUNY Polytechnic Institue
Utica, NY
2020 - 2019
- Organizing 100 and 200 level mathematcs courses and supervising instructors teaching them
Associate Instructor
Indiana University
Bloomington, IN
2015 - 2008
- Teaching Assistant
- 100 and 200 level mathematics courses
Education
PhD., Applied Mathematics & M.A., Mathematics
Indiana University
Bloomington, IN
2015 - 2008
- Focused on data assimilation and determining forms for semidissipative dispersive systems
- Associate Instructor
B.S., Mathematics
Boğaziçi University
Istanbul, Turkey
2008 - 2003
- Mathematics
Data Science Skills
Supervised Learning
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- Multiple Linear Regression, Multiple Logistic Regression, Polynomial Regression, Regularized models, KNN regression and classification, Tree Based Methods (Decision Trees, Random Forest, Boosted Trees), Support Vector Machines, Naive Bayes Classifier;
- R packages: knn, e1071, tree, rpart, glm, glmnet, kernlab, tidymodels
Unsupervised Learning
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- k-means clustering, Hierarchical clustering , PCA
- R packages: tidymodels
Data cleaning & Feature Engineering
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- Numerical and categorical variable imputation, variable transformation, filtering
- R packages: tidyverse packages, tidymodels (recipe)
Time Series Analysis
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- ARIMA, SARIMA
- R packages: astsa, forcast, lubridate
Reports/dashboard
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- R Markdown, R Quarto, R Shiny
- Sample R Shiny web app: https://turalsadigov.shinyapps.io/my_new_ames_app/
Research Experience
Summer Research Associate
Air Force Research Lab Griffis Institute
Rome, NY
2021
- Worked on theoretical neural network solutions of dispersive equations
- Supervised a student who worked with me
Summer Research Associate
Air Force Research Lab Griffis Institute
Rome, NY
2020
- Worked on numerical algorithms to solve partial differential equations
Awards/Grants
Hamilton College
Dean’s Pedagogical Development Award (twice)
Clinton, NY
2022 - 2021
Air Force Reserach Lab (AFRL/RI)
Summer research grants (twice)
Rome, NY
2021 - 2020
SUNY Polytechnic Institue
SGU Award for Excellence in Teaching
Utica, NY
2019 - 2018
Coursera
Teaching Grant (Practical Time Series Analysis)
Online
2017
IMO 2003
Bronze medal
Tokyo, Japan
2003
Ministry of Science and Education Republic of Azerbaijan
Four Gold medals
Baku, Azerbaijan
2003 - 2000
IMO 2002
Participation
Glasgow, Scotland
2002
Selected Mentored ML Projects
Does Economic Development Predict Democratization?
Chiara Bondi, John Madigan
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2022
- Regularized Regression Models (LASSO, Ridge, Elastic-Net)
A Regularized Binomial Logistic Regression Approach to Cancer Classification Using Gene Expression
Joshua Horowitz, Mukund Jayaran
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2022
- Regularized Logistic Regression
Predicting Career Longevity of NBA Rookies
Margaret Phipps, Luke Devine,
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2021
- Support Vector Machines
Identifying Parkinson’s Disease Through Speech Patterns
Ian Nduhiu, Lindsay Gearty,
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2021
- Support Vector Machines
Predicting Housing Rent Prices Using House Characteristics
Taron Kui, Iftikhar Ramnandan, Jenny Tran
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2021
- Decision Trees, Bagged Models, Random Forest
How Race, Income, and Education Relate to Inter- net Access in US Counties
Lindsay Gearty, Margraet Phipps,
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2021
- Multiple Polynomial Regression, Forward/Backward Variable Selection
Publications
Safety Prediction Model for Reinforced Highway Slope using a Machine Learning Method
Transportation Research Record: Journal of the Transportation Research Board
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2020
- Authored with Asif Ahmed, Sadik Khan, Sahadat Hossain and Prabesh Bhandari
A determining form for the subcritical surface quasi-geostrophic equation
Journal of Dynamics and Differential Equations (JDDE)
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2019
- Authored with Michael S. Jolly, Vincent R. Martinez and Edriss S. Titi
Determining form and data assimilation algorithm for weakly damped and driven Korteweg-de Vries equaton- Fourier modes case
Journal of Nonlinear Analysis-B: Real World Applications
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2017
- Authored with Michael S. Jolly, and Edriss S. Titi
A determining form for the damped driven Nonlinear Schrödinger Equation- Fourier modes case
Journal of Differential Equations
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2015
- Authored with Michael S. Jolly, and Edriss S. Titi